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Multi-label Learning by Exploiting Imbalanced Label Correlations
2021Multi-label classification refers to the supervised learning problem where an instance may be associated with multiple labels. It is well known that exploiting label correlations is important for multi-label learning. Existing approaches typically assume that the distribution of classes is balanced. In many real-world applications, multi-label datasets
Shiqiao Gu +3 more
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An Multi-Label Classification with Label Correlation
Asian Journal of Research in Social Sciences and Humanities, 2016Nowadays multi-label data are numerously available in real-world applications. Multi-label data instances are associated with more number of class labels at same time. Generally, the multi-label classification is done in many ways. Recognize of label correlation in multi-label data is difficult.
S. Sabena +3 more
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Label correlations-based multi-label feature selection with label enhancement
Engineering Applications of Artificial IntelligenceChi-Man Vong +2 more
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Exploring Label Correlations for Partitioning the Label Space in Multi-label Classification
2021 International Joint Conference on Neural Networks (IJCNN), 2021Recent works on Multi-Label Classification (MLC) present multiple strategies to explore label correlations in a way to improve classifiers performances. However, these works focus only in the traditional local and global approaches, i.e., transforming the original problem into a set of binary local problems, or dealing globally with all classes ...
Elaine Cecília Gatto +2 more
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Correlation Clustering with Stochastic Labellings
2013Correlation clustering is the problem of finding a crisp partition of the vertices of a correlation graph in such a way as to minimize the disagreements in the cluster assignments. In this paper, we discuss a relaxation to the original problem setting which allows probabilistic assignments of vertices to labels. By so doing, overlapping clusters can be
Nicola Rebagliati +2 more
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Multi-label Learning by Exploiting Label Correlations with LDA
2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI), 2017In multi-label learning, each object is represented by a single instance while associated with a set of class labels, and labels often have correlations with each other. Exploiting label correlations can improve the performances of classifiers. Current multi-label classification methods mainly consider the correlations from label pairwise or label ...
Yue Peng +4 more
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Generative Multi-Label Correlation Learning
ACM Transactions on Knowledge Discovery from Data, 2023In real-world applications, a single instance could have more than one label. To solve this task, multi-label learning methods emerged in recent years. It is a more challenging problem for many reasons, such as complex label correlation, long-tail label distribution, and data shortage.
Lichen Wang +6 more
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Clustered intrinsic label correlations for multi-label classification
Expert Systems with Applications, 2017The classifier for each label consists of a label-specific part and a shared one.The label-specific part characterizes the corresponding label.The shared part represents the information shared by all labels.Intrinsic label correlations are represented by label-specific parts.The proposed method extends SVM to the multi-label setting.
Jujie Zhang, Min Fang, Xiao Li 0008
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Multi-label Classification with Label Correlations of Multimedia Datasets
2016In multi-label classification tasks, very often labels are correlated and to not lose important information, methods should take into account existing dependencies. Such situation especially takes place in the case of multimedia datasets. In the paper, universal problem transformation methods providing for label correlations are considered.
Kinga Glinka, Danuta Zakrzewska
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Multi-Label Deep Active Learning with Label Correlation
2018 25th IEEE International Conference on Image Processing (ICIP), 2018Annotating a data sample in a multi-label learning problem requires a human oracle to consider the presence/absence of every possible label separately, which is extremely labor intensive. Active learning algorithms automatically identify the informative samples from large amounts of unlabeled data and significantly reduce human annotation efforts in ...
Hiranmayi Ranganathan +3 more
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